Zustandsklassifikation von nichtlinearen dynamischen Systemen mit Zellularen Neuronalen Netzen und mit Untersuchung des Phasenskalierungsverhaltens
Zustandsklassifikation von nichtlinearen dynamischen Systemen mit Zellularen Neuronalen Netzen und mit Untersuchung des Phasenskalierungsverhaltens
dc.contributor.advisor | Lehnertz, Klaus | |
dc.contributor.author | Florin, Sascha Alexander | |
dc.date.accessioned | 2020-04-06T19:48:16Z | |
dc.date.available | 2020-04-06T19:48:16Z | |
dc.date.issued | 2004 | |
dc.identifier.uri | https://hdl.handle.net/20.500.11811/2059 | |
dc.description.abstract | During the last years different methods from non-linear time series analysis have been successfully applied to classify the dynamics of complex systems in a number of disciplines, including physics, astrophysics, biology, chemistry, and the neurosciences. One of the most challenging complex systems is the brain. Here, pathological alterations like epilepsy introduce non-linear deterministic structures in an otherwise linear stochastic background activity. In the present study the dynamics of the epileptic brain was examined by using a Cellular Neural Network (CNN) and by determining the scaling properties of a phase variable. The aim was a classification of the spatio-temporal dynamics and, in particular, to discriminate in time between inter-seizure and pre-seizure states as well as in space between the epileptic focal and non-focal hemisphere. Time series of brain electrical activity (EEG) with different temporal dynamics but with a similar visual appearance could be distinguished using a CNN without reducing the information content of these time series. A spatial classification was based on the scaling properties of a phase variable estimated for band-pass filtered multi-channel EEG recordings in order to examine scale invariance and persistence. A characteristic scaling behaviour of the phase, with persistence in all classical EEG frequency bands, could be observed which allowed to distinguish the focal from the non-focal hemisphere in all investigated patients. The time series analysis techniques investigated here might provide further insights into the spatio-temporal dynamics of complex systems other than the epileptic brain. | |
dc.language.iso | deu | |
dc.rights | In Copyright | |
dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | |
dc.subject | Zeitreihenanalyse | |
dc.subject | nichtlineares System | |
dc.subject | epileptisches Gehirn | |
dc.subject | EEG-Dynamik | |
dc.subject.ddc | 500 Naturwissenschaften | |
dc.subject.ddc | 530 Physik | |
dc.subject.ddc | 610 Medizin, Gesundheit | |
dc.title | Zustandsklassifikation von nichtlinearen dynamischen Systemen mit Zellularen Neuronalen Netzen und mit Untersuchung des Phasenskalierungsverhaltens | |
dc.type | Dissertation oder Habilitation | |
dc.publisher.name | Universitäts- und Landesbibliothek Bonn | |
dc.publisher.location | Bonn | |
dc.rights.accessRights | openAccess | |
dc.identifier.urn | https://nbn-resolving.org/urn:nbn:de:hbz:5N-03901 | |
ulbbn.pubtype | Erstveröffentlichung | |
ulbbnediss.affiliation.name | Rheinische Friedrich-Wilhelms-Universität Bonn | |
ulbbnediss.affiliation.location | Bonn | |
ulbbnediss.thesis.level | Dissertation | |
ulbbnediss.dissID | 390 | |
ulbbnediss.date.accepted | 12.07.2004 | |
ulbbnediss.fakultaet | Mathematisch-Naturwissenschaftliche Fakultät | |
dc.contributor.coReferee | Maier, Karl |
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